[hydrology] research and development of remote sensing methods

7/28/2019 [Hydrology] Research and Development of Remote Sensing Methods

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Contents

1. Introduction.........................................................................................................................................3

2. Snow-signature review........................................................................................................................4

2.1 Active microwave signatures ......................................................................................................4

2.2 Passive microwave signatures .....................................................................................................6

2.3 Visible and infrared signatures....................................................................................................7

3. Papers discussing snowpack measurements........................................................................................9

3.1 Active microwave measurements................................................................................................9

3.2 Passive microwave measurements.............................................................................................21

3.3 Combined active and passive microwave measurements..........................................................24

3.4 Dielectric measurements and models ........................................................................................29

3.5 Visible and infrared measurements and models........................................................................33

4. Identified problem areas and data gaps.............................................................................................37

5. References.........................................................................................................................................40

5.1 Microwave and dielectric measurements ..................................................................................405.2 Visible and infrared measurements ...........................................................................................49

Snow-Tools Project Participants ............................................................................................................51

1.


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Introduction

The objectives of the WP 310 are threefold. In a first step, the user needs for electromagnetic

signatures of snowpacks are to be assessed. Then, available signature data sets have to be reviewed,enabling to identify data gaps and to plan further measurements. In the following, the term

"microwave signature" describes the characteristic behavior of a surface type, whose state is

sufficiently well known, with respect to its interaction with microwave radiation dependent on

frequency, polarization and observation geometry,

The present documentation is a compilation of electromagnetic signatures of snowpacks in the optical

and microwave range, with emphasis on active and passive microwave part. The review was guided

by [1] and [2], and complemented with works of the last three years. Special attention should be also

paid to EMAC (1995), though results are not yet available from this campaign. We analyzed in

particular the signatures obtained from in-situ and airborne measurements. Spaceborne campaigns are

considered only if extensive ground-information was collected simultaneously and if atmospheric

influence was addressed. A review of models was also accomplished, with the goal to identify the

signature measurements used to validate the models.

The present documentation is a list of available data sets containing snowpack signatures (Section 2),a list of papers discussing snowpack measurements (Section 3) and a list of identified data gaps

(Section 4). In Section 2 we list works containing quantitative indications of the measured variables,

which can be directly used for a general signature catalogue. Active and passive microwave

measurements are analyzed separately. In Section 3 short descriptions of the instruments, the test-

sites, the ground-information and the main investigation are given for papers discussing snowcover

measurements. Papers describing measurements performed with radars, radiometers and concurrently

with active and passive microwave sensors are presented in separate subsections. Papers dealing with

dielectric properties of the snowpacks are listed in a separate subsection as well. In Section 4 we

indicate data gaps and problem areas which are evident from the analyzed papers.

It should be noted that the term "ground information", which is used throughout the following

sections, is used in the meaning of "informations gathered independently from the microwave

measurements".

2.


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Snow-signature review

2.1 Active microwave signatures

An extensive effort in order to obtain a complete signature catalogue of terrain was performed by

Ulaby and Dobson [2]. The statistical behavior of the radar measurements performed by different

research groups using different instruments are summarized for several terrain categories. However,

for snowcover only two categories were defined: dry and wet snow. Wet snow is defined as snow

with a liquid-water content larger than 1% by volume. This is not an adequate definition for dry snow,

where the liquid water content is well below 0.1% (cf. Fig. 1). No minimum or maximum snow depth,

no surface parameters and no ground conditions were given.

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.501.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

E'

snow density (g/cm3)

Dry SnowFit

Liquid Water:1% by Volume

Figure 1: Measured permittivity of dry snow versus measured density. The solid line is the fit by

d' = 1+1.5995+1.8613 (


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collected in a catalogue [3]. Most of the measurements were made at a test-site above Davos in the

Swiss Alps at 2540 m above sea level (a.s.l.), but studies were conducted also at other test-sites in

Switzerland and Austria at altitudes between 500 and 2200 m a.s.l. The backscattering coefficient

was measured at hh-, vv-, hv-and vh-polarization together with physical parameters of the snowcover,like snow height, stratification, temperature, density and permittivity.

Based on ground information and on a simple distribution ofsignatures different object classes were

identified in [4]. The signatures were used in order to evaluate the capability of active microwave

sensors at 5.3 and 35 GHz for the classification of snowcovers. In addition, semi-empirical algorithms

for the retrieval of physical parameters of the snowcover, such as water equivalent, liquid-water

content and thickness of the refrozen crust, were defined.

Extensive radar backscattering experiments were conducted at 35 and 94 GHz [5] in order to measure

the response of snow-covered ground to snow depth, liquid-water content and grain size. The

measurements included observations over a wide angular range extending between normal incidence

and 60 for all linear polarization combinations. A numerical radiative transfer model [6] was

developed and adapted to fit the experimental observations. Next, the radiative transfer model was

exercised over a wide range of conditions and the generated data was used to develop relatively

simple semi-empirical expressions that relate the backscattering coefficient (for each linearpolarization) to incidence angle, snow depth, grain size, and liquid-water content. Although applicable

only for homogeneous snowcovers, this simple semi-empirical model permits a reasonable estimation

of the snow signatures for a wide range of situations at 35 and 94 GHz. The effect of the underlying

ground is not taken into consideration, because it is discussed in a further publication [8], where

appropriate models were developed in order to relate the backscattering coefficients to soil surface

and volume properties.

In another experimental approach, homogenous, dry snow slabs were investigated in order to get the

extinction behavior of dry snow at 10, 18, 35, 60 and 90 GHz [9], [10]. These measurements are

useful in order to elaborate the quantitative relationships between snow properties and microwave

signatures. A free-space transmission system with a variable distance between transmitting and

receiving antennas of 60 to 75 cm was set up. Different natural snow types ranging from newly fallen

snow to refrozen snow with variable thickness comprised between 1 and 20 cm were measured at

HUT. Ground information included average grain size, surface roughness and density. Relationships

between extinction loss and snow sample thickness, extinction coefficient between snow particle size


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and extinction coefficient between frequency were investigated. A comparison between the

experimental extinction coefficients and theoretical analysis was also performed.

Further extensive signature studies with ground-based, airborne and space-borne microwave systemswere performed at the University of Innsbruck [e.g. 83]. Data from this research team can presumably

be directly used for a the signature catalogue. However, we have not yet investigated this possibility,

and papers from Rott et al. (including works performed by Shi as first author) are now listed in

Section 3.

2.2 Passive microwave signatures

An extensive signature catalogue of passive microwave measurements was prepared by Mtzler [11],

[12]. The behavior of ground-based measured emissivities at 4.9, 10.4, 21, 35, 94 GHz, linear

horizontal and vertical polarization, and incidence angles between 50 and 75 are discussed. The

catalog includes spectral and angular plots of the reflectivities together with complete ground

information. Bare soil, grass, oat and barley canopies with and without snowcover on frozen and

unfrozen ground were measured at Moosseedorf, about 10 km north of Bern and at 570 m a.s.l. Alpine

snowcovers under various conditions were investigated at Weissfluhjoch, Davos, at 2540 m a.s.l. near

the Swiss Federal Institute of Snow and Avalanche Research. (SFISAR) [12,60].

In [13] the signatures of landscapes in winter at 50 incidence angle were identified. Mean values for

object classes were computed. The discussion of the behavior of the emissivities versus frequency

lead the author toward a classification algorithm for almost all object classes. Difficulties occurred

with fresh powder snow if 94 GHz data were not available. The problem of wet snow has found a

solution by using a certain combination of observables. The applicability of the signatures for the

estimation of physical parameters like snow coverage, snow liquid water content, water equivalent of

dry snow was also investigated. The author found that the estimation of the surface temperature,

especially for snow-free land, and of the liquid-water content at the surface from passive

measurements seem to be feasible. Lower frequencies (e.g. 1.4 GHz) should be used in order to

estimate soil moisture. For the estimation of the water equivalent a solution using the polarization

difference is proposed.

A further development from the signature studies [11] and [12] lead to another extensive passive

microwave signature catalogue [14]. A multi-frequency system based on portable radiometers was


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operated on several locations in the Alps and in the Swiss Central Plains. The system covered the

frequency range from 11 to 94 GHz. The temporal and spatial behavior of the emissivity and

brightness temperature was investigated for different snow and snow-free situations. The passive

microwave measurements were complemented by ground observations. The ground informationincludes temperature, permittivity, density, and wetness profiles.

In an experimental approach in order to derive the microwave emission as a function of snow

structure [15], [16], [17], homogenous, dry snow slabs were investigated. These measurements are

very useful in order to elaborate in detail the quantitative relationships between snow properties and

microwave signatures. The measurements were performed during the 1993/94, 1994/95 and 1995/96

winters outdoor at the alpine test-site Weissfluhjoch. Homogeneous samples of dry snow with a

typical size of 45 x 45 x 10 cm3 were cut within the natural snowcover and investigated. A procedure

for computing the radiometric properties (expressed as emissivity, transmissivity and reflectivity)

from the measured brightness temperatures was presented. Digitized snow sections were used in order

to characterize the snow samples by their three-dimensional autocorrelation function. The data show

that the radiometric quantities are clearly sensitive to snow structure, i.e. they depend on the

correlation length. A first comparison between experimental results and model simulations according

to the "strong fluctuation theory" was performed.

2.3 Visible and infrared signatures

A basic signature of snow is its high reflectivity, also called reflectance or spectral albedo, in the

visible part of the spectrum, leading to a significant reduction of absorption of solar radiation. The

presence of any light-absorbing impurity reduces the spectral albedo of pure snow. With increasing

wavelength towards the near infrared, the spectral albedo decreases and at the same time, it becomes

sensitive to the grain size - or more exactly - to the specific surface of the snowpack, whereas

impurities become less important, especially beyond 900nm. In the thermal infrared snowpacks are

nearly black bodies. A review of these optical snow properties including their modeling with

simplified radiative transfer was presented by Warren (1982). Up today the model of Wiscombe and

Warren (1980) has been the standard for the entire solar spectrum. This model suggests that for pure

snow the grain size is the controlling parameter and that snow density is unimportant. Experimental

results confirmed the model, see e.g. Grenfell et al. (1981) and references cited therein. Further

modeling include the infrared range (Dozier and Warren, 1982; and Wald, 1994), and directional

effects were measured by Hall et al. (1993).


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The most important optical property of ice, which causes spectral variation in the reflectance of snow

in visible and near-infrared wavelengths, is that the absorption coefficient (i.e. the imaginary part of

the refractive index) varies by seven orders of magnitude at wavelengths from 0.4-2.5 micrometers.

The presence of liquid water in the snow does not by itself greatly affect the reflectance. The changesin reflectance that occur in melting snow result mainly from the increased grain sizes. At visible

wavelengths, reflectance is insensitive to grain size, but is affected by two variables, finite depth and

the presence of absorbing impurities.

The optical and near IR instruments are used to derive snow area based on the visible appearance of

snow, which is vastly different from most other natural surface types. The combined use of visible

and near-infrared wavelengths has been the most successful approach to mapping snow cover.

However, these techniques are not without their problems and discrimination of snow cover from

clouds is and will remain a major problem.

New work undertaken by Salisbury (Salisbury et al., 1994) has provided spectral information for

various snow covers at VNIR, SWIR, MWIR and TIR that can be used as the basis for the signature

database along with field campaign and past satellite work.

3.


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Papers discussing snowpack measurements

3.1 Active microwave measurements

The papers are listed in alphabetical order. A short description of the instruments, the test-sites, the

ground-information and the main investigation is given. Selected highlights were extracted from the

corresponding papers. Full references including page numbers are given in Chapter 5.1.

[18] Millimeter-wave backscatter measurements on snow-covered terrain

Baars E.P., H. EssenIEEE Trans. Geosci. Remote Sensing, Vol. 26, No. 3, 1988.

instrument: polarimetric radar 94 GHzdepression angle: 15 - 55polarization: circular LL, LRheight above ground: 31.5 mradar scans continuously in azimuth angle

sample: snowcover freshly fallen, aging nonmetamorphic, aging metamorphiclocation: flat snow area, 130 m by 60 m.

valley in German Alps,plateau in Eifel mountains, northwest Germany

ground information:air temperature, snow depth, surface state, type of crystal, layer structure(density, hardness index, temperature), liquid water content

investigations: reflectivity vs. depression angle;reflectivity vs. liquid water content;mean reflectivity vs. time (several days) for different snow conditions;spatial variations

remarks: -

[19] Observations of the backscatter from snow at millimeter wavelengths

Berger R., Layman R., Van Zandt T., Walsh J., Knox J.

In: Snow Symposium V, Hanover, New Hampshire, August 1985, Vol. 1, U.S. Army ColdRegions Research and Engineering Laboratory, Hanover, NH, CRREL Special Report 86-15,pp. 311-316.

remarks: Paper not available.

[20] The Potential of Time Series of C-Band SAR Data to monitor dry and shallow snow

cover

Bernier M., J.-P. FortinSubmitted to IEEE Trans. Geosc. Rem. Sens., 1995


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instrument: C-Band SAR 5 GHzConvair-580 of the Canada Centre for Remote Sensingpolarization: HHincidence angle: 45 - 74airborne-based

sample: snowcover dry, wet, snow-free groundlocation: watershed in the Appalachian Mountains in Southern Quebec (Canada)ground information:depth, density, snow water equivalent, liquid water content, temperature

and dielectric profileinvestigations: backscattering power ratio vs. snow water equivalent

backscattering power ratio vs. soil surface temperaturebackscattering power ratio vs. thermal resistance of the snow cover

remarks: estimation of the liquid water content by means of the ratio of thescattering coefficient of a field covered by snow to the scatteringcoefficient of a field without snow

[21] Two-parameter backscatter model of snowcover at millimeter wavelengths

Chang P., J. Mead, S. Lohmeier, P. Langlois, R. McIntoshProc. 12th Annual International Geoscience & Remote Sensing Symposium IGARSS '92, May26-39, Houston, Texas. pp. 1667-1669.

instrument: polarimetric radar 225 GHzincidence angle: 25 ,60 - 80height above ground: 25 m

sample: snowcover dry, refrozen

location: athletic field and sloping hillside, Amherst, MA, USAground information:gravimetric liquid water content, snow density, surface roughness,

particle sizeinvestigations: polarization synthesisremarks: + data at 95 GHz

a two parameter model was developed for snowcover consisting of nearspherical crystals

[22] A Detailed Study of the Backscatter Characteristics of Snowcover Measured at 35, 95

and 225 GHz

Chang P.S., J.B. Mead, R.E. McIntoshProceedings of IGARSS, Pasadena, CA, pp. 1932-1934, 1994.

instrument: polarimetric radar 35, 95 225 GHzincidence angle: 60 - 80height above ground: 24 m

sample: snowcover melt-freeze cycleslocation: athletic field and sloping hillside, Amherst, MA, USAground information:detailed in-situ data including microstructural anisotropies within the

snowpackinvestigations: normalized radar-cross section, correlation coefficient, average phase


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differenceremarks: comparison with a simple radiative transfer model

[23] Polarimetric Backscatter from Fresh and Metamorphic Snowcover at Millimeter

Wavelengths

Chang P.S., J.B. Mead, E.J. Knapp, G.A. Sadowy, R.E. Davis, R.E. McIntoshIEEE Trans. on Antennas and Propagation, Vol. 44, No. 1, pp. 58-73, 1996.

instrument: polarimetric radar 35, 95 225 GHzincidence angle: 60 - 80height above ground: 24 m

sample: snowcover melt-freeze cycleslocation: athletic field and sloping hillside, Amherst, MA, USAground information:detailed in-situ data including microstructural anisotropies within the

snowpackinvestigations: normalized radar-cross section, correlation coefficient, average phase

differenceremarks: comparison with a simple vector radiative transfer model

[24] Millimeter-wave measurements and analysis of snow-covered ground

Currie N.C., J.D. Echard, M.J. Gary, A.H. Green, T.L. Lane, J.M. TrostelIEEE Trans. Geosci. Remote Sensing, Vol. 26, No. 3, 1988.

instrument: radar 35, 94 GHzdepression angle: 13 - 35 [tower]

10 - 60 [airborne]raster scans in azimuthpolarization: HH, VV, HV, VH [tower]

RR, LL, RL, LR [airborne]simultaneous tower and airborne testsheight above ground: 30 m [tower]

200, 400, 800 ft [airborne]sample: snowcover multiple snow conditionslocation: Houghton, MI, USA

ground information: liquid water content, surface roughness, air and snow temperature, snowdepth, density, grain size and type

investigations: backscattering coefficient as function of wavelength, coherentbandwidth, polarization, incidence angle;diurnal measurements

remarks: SNOWMAN test program by US Army and Georgia Techpresentation of the data collection procedure and of examples of resultsidentification of gaps in the data

[25] The Use of Microwave FMCW Radar in Snow and Avalanche Research

Gubler H. and M. Hiller


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Cold Regions Science and Technology, 9 (1984), pp. 109-119.

instrument: FMCW radar at X-Bandeither buried in the ground looking into the snow cover or towed on skislooking downward into the snow

sample: snowlocation: Weissfluhjoch, Davosground information:-investigations: estimation of the height of dense flow in avalanches

determination of the geometrical layering, density, water equivalence,settlement, total snow height, percolation of water and moisture content

remarks: -

[26] 140-GHz scatterometer system and measurements of terrain

Haddock T.F., F.T. UlabyIEEE Trans. Geosci. Remote Sensing, Vol. 28, No. 4, 1990.

instrument: scatterometer at 140 GHzincidence angle: 0 - 70polarization: HH, VV, HV, VHtruck-mounted

sample: grasses, trees, snowlocation: near Ann Arbor, MI, USAground information:-

investigations: backscattering coefficient vs. incidence angle for different targets andfrequencies

remarks: sample measurements in order to test the system

[27] Radar Polarimeter Measurements of Snow

Hallikainen, M., Pulliainen, J.,Digest 1989 IEEE Inernational Geoscience and Remote Sensing Symposium (IGARSS'89), pp.1829-1831, Vancouver, Canada, 10-14 July 1989.

instrument: NA-based scatterometer at 35 GHzincidence angle: 0 - 60polarizations: RCP-V, RCP-H, LCP-V, LCP-Hmeasurements from rooftop and using a movable boom (height 15 m)

sample: dry and wet snowlocation: Southern Finlandground information:snow density, water content, grain size, snow depth, temperatureinvestigations: backscattering coefficient vs. incidence angle

polarization synthesisremarks: no further experiments with the NA-based scatterometer at 35 GHz were

performed at HUT

[28]


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Scattering from snow backgrounds at 35, 98, and 140 GHz

Hayes D.T., U.H.W. Lammers, R.A. MarrReport RADC-TR-84-69, Rome Air Development Center, Air Force Systems Command, GriffisAir Force Base, New York, 1984.

instrument: CW scatterometer at 35, 98 and 140 GHzgrazing angle: 15, 45, 90polarization: HH, VV, HVcontinuous azimuthal sweep

sample: snowcover melting, refreezinglocation: flat snow field, 66 m a.s.l.ground information:depth, density, hardness, temperature, stratigraphy, microstructure,

surface characteristics, liquid-water contentinvestigations: averaged backscattering coefficient of dry and wet snow vs. grazing angle

averaged backscattering coefficient of dry and wet snow vs. frequencyremarks: grazing angle = 90 - nadir angle

[29] "Radar Measurements on Artificial Snow of Varying Depth" in Microwave Remote

Sensing of Snow: An Empirical/Theoretical Scattering Model for Dense Random Media

Kendra J.R.Ph.D.-Thesis, Department of Electrical Engineering and Computer Science, The University ofMichigan, 1995.

instrument: frequency: 1.25, 5.3 and 9.5 GHzpolarization: full polarizedgrazing angle: 20-60truck mounted

sample: artificial snow dry and melt-refreeze cycleslocation: Mt. Brighton Ski Area, Michiganground information:extensiveinvestigations: backscattering coefficient vs. incidence angle for different frequencies

and depths of dry snowcomparison with discrete-particle-based theories for dry snow

diurnal variation of the backscattering coefficient for wet snowevaluation of a wetness retrieval algorithm (Shi and Dozier, 1995)remarks: includes also "Snow Probe for In Situ Determination of Wetness and

Density" and "A Hybrid Experimental / Theoretical Scattering Model fora Dense Random Media"

[30] Millimeter-wave polarimetric radar scattering from snow

Kuga Y., A. Nashashibi, F.T. UlabyIGARSS '91

instrument: polarimetric radar at 35 and 94 GHz


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coherent on-receive at 6 polarization statestruck mounted

sample: snow-covered terrainlocation: ?ground information:snow liquid water content

investigations: polarization synthesis: Mueller matrix and degree of polarization as afunction of incidence angle and terrain roughnessdiurnal measurements

remarks: abstract only

[31] Millimeter-wave multipath measurements on snow cover

Lammers U.H.W., D.T. Hayes, R.A. MarrIEEE Trans. Geosci. Remote Sensing, Vol. 26, No. 3, 1988.

instrument: radar at 35.1, 98.1 at 140.1 GHzheight-gain patterns between 0.2 and 4 mpathlength 179.5 mgrazing angle: 0.5 - 2

sample: snowcover frozen, dry, freshly fallenmatted grass

location: plane field, grass cut during summerground information:air and snow temperature, density, grain size, snow depthinvestigations: effect of different snow types and depthsremarks: measure of the interference patterns

[32] Permittivity and attenuation of wet snow between 4 and 12 GHz

Linlor W.I.Journal of Applied Physics, Vol. 51(5), May 1980, pp. 2811-2816.

instrument: 2 pairs of microwave horns f = 4-6, 6-8 and 8-12 GHz, network analyzer

sample: wet snowlocation: laboratory conditionsground information:-investigations: permittivity and attenuation of prepared wet snow samples, empirical

relations between attenuation and wetness at frequency between 4 and 12GHz

remarks: -

[33] A comparison of normalized radar cross section measurements and models for snow

cover at 35, 95 and 225 GHz

Lohmeier S.P., P.M. Langlois, J.G. Colom, R.E. Davis, H.S. Boyne, R.E. McIntoshIGARSS '92, pp. 1655-1657.

instrument: polarimetric radar at 35, 95 and 225 GHzincidence angle: 20, 40, 60 [35], 25 - 80 [95,225]


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polarization: VV, VH [35], VV,HH,VH,HV [95,225]sample: snowcoverlocation: terrain of medium roughness:

Hanover, NH, USA [35]Amherst, MA, USA [95,225]

ground information: liquid water content, surface roughness, layer thickness, snowtemperature, crystal size and type, density

investigations: normalized radar cross section (NRCS) versus incidence angleremarks: comparison with theoretical model

[34] Review of the Radar Experiments of the Seasonal Snow Cover

Mtzler C.Workshop on the Interaction of Microwaves with the Seasonal Snow Cover, CRREL, October17 -19 1984

instrument: review of ground-based and airborne experimentsemphasis on radar measurements made at 10.4 GHz at Weissfluhjochcomparison with the radar imagery obtained during a SAR experiment

sample: snowcoverground information:Operational data collected by SFISARinvestigations: backscattering coefficient vs. incidence angle

SAR imageryremarks: Simultaneous passive microwave observations

[35] Polarimetric scattering from natural surfaces at 225 GHz

Mead J.B., P.M. Langlois, P.S. Chang, R.E. McIntoshIEEE Trans. Antennas Propagation, Vol. 39, No. 9, 1991.

instrument: noncoherent polarimetric radar at 225 GHz6 combinations of linear and circular polarization

sample: natural surfaces like trees, grass, snowcover and sandlocation: Amherst, MA, USAground information:-investigations: measurement of Mueller matrix and the depolarization ratio;

degree of polarization vs. depolarization rationormalized Mueller matrices of a limited class of natural targets may beclosely predicted by a single parameter, the depolarization ratio

remarks: summary of various polarimetric quantities for a variety of naturaldistributed targets

[36] Polarimetric observations and theory of millimeter-wave backscatter from snow cover

Mead J.B., P.S. Chang, S.P. Lohmeier, P.M. Langlois, R.E. McIntoshIEEE Trans. Antennas Propagation, Vol. 41, No. 1, 1993.

instrument: noncoherent polarimetric radar at 95 and 225 GHz6 combinations of linear and circular polarization


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incidence angle: 55-80height above ground: 25 m

sample: fresh and refrozen snowcoverlocation: Amherst, MA, USAground information:measured following standard procedures

investigations: measurement of the Mueller matrixdepolarization ratio, degree of polarization, phase differences

remarks: analysis of backscatter from snowcover consisting of spherical iceparticles

[37] Millimeter-wave backscatter characteristics of multilayered snow surfaces

Narayanan R.M., R.E. McIntoshIEEE Trans. Geosci. Remote Sensing, Vol. 38, No. 5, 1990.

instrument: pulsed radar at 215 GHzincidence angle: 25 - 45, 66 [rooftop], 75.6, 83.2polarization: HH, VV, HV, VHheight above ground: 80 m

sample: snowcover winter seasonlocation: snowfield and rooftop

Amherst, MA, USAground information:surface roughness, moisture content, density, hardness, temperature, layer

thickness, grain size and typeinvestigations: normalized radar cross section vs. incidence angle;

effect of wetness, roughness, density, grain size

remarks: comparison with a simple model based on geometrical optics and Miescattering theory

[38] Temporal Variations in Radar Backscatterer Coefficients of Vegetation and Snow

Cover

Nystrom A., A. Stjernman, J. VivekanandenProceedings of IGARSS'94, pp. 2483-2485.

instrument: NA-based scatterometer between 1 and 18 GHzincidence angle:polarization: HH, VV, HV, VHheight above ground: 17 m

sample: birch trees and multilayered snowlocation: Kiruna, Swedenground information:-investigations: estimation of snow pack water equivalent

diurnal variations of the scattering coefficientangular variation of the scattering coefficient

remarks: preliminary analysis


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[39] Multifrequency and Polarimetric SAR Observations Alpine Glaciers

Rott H., R.E. Davis

Annals of Gaciology, 17, 1993

instrument: AIRDAR (440 MHz, 1.25 GHz, 5.3 GHz, polarimetric)sample: snowcover and glacierslocation: Rofental, Austriaground information:dielectric, structural and surface roughness propertiesinvestigations: seasonal variations of the backscatteringremarks: comparison with Landsat TM and SPOT

[40] Capabilities of ERS-1 SAR for Snow and Glacier Monitoring in Alpine Regions

Rott H., T. NaglerProceedings of the Second ERS-1 Symposium, 11-14 October 1993, Hamburg, Germany

instrument: ERS-1 SAR (5.3 GHz, VV-Pol., 23 incidence angle)sample: snowcover and glacierslocation: Innsbruck-Leutasch and tztal, Austriaground information:snow depth, standard deviation of surface roughness, volumetric liquid-

water content, snow temperatureinvestigations: seasonal variations of the backscatteringremarks: procedure for mapping the extent of melting snow

[41] Snow and Glacier Parameters Derived from Single Channel and Multi-Parameter SAR

Rott H., T. Nagler, D.-M. FloricioiuInternational Symposium on the Retrieval of Bio- and Geophysical Parameters from SAR Datafor Land Applications. Toulouse, France, 10-13 October 1995

instrument: ERS-1 (5.3 GHz, VV-Pol., 23 incidence angle)SIR-C/X-SAR (1.25 and 5.3 GHz, polarimetric; 9.6 GHz, VV-Pol.;incidence angle between 15 and 60)

sample: snowcover and glacierslocation: tztal, Austriaground information:standard deviation of surface roughness, median value of surface

correlation length, volumetric liquid-water content, mean grain diameter,density

investigations: backscattering signatures of snow-covered areas and of glacierse.g. angular dependence of the backscattering

remarks: summary on the use of SAR for snow and glacier applications

[42]


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Microwave Snowpack Studies Made in the Austrian Alps During the SIR-C/X-SAR Experiment

Mtzler Ch., Strozzi T., Weise T., Floricioiu D.-M., Rott. H.Int. J. Remote Sensing, in press (1997)

instrument: SIR-C/X-SAR (1.25 and 5.3 GHz), polarimetric;Polarimetric scatterometers at 5.3 and 35 GHzRadiometers at 21 and 35 GHzdielectric probes

sample: snowcover and glaciers in the Austrian Alpslocation: tztal, Austriaground information: liquid water profiles of the snowpacks, snow-physical observationsinvestigations: backscattering and emission signatures of snow-covered areas and of

glaciers, temporal variationsremarks: -

[43] Inferring Snow Wetness Using C-Band Data from SIR-C's Polarimetric Synthetic

Aperture Radar

Shi J. and J. DozierIEEE Trans. Geosc. Rem. Sens., Vol. 33, No. 4, July 1995

instrument: SIR-C-SAR (5.3 GHz, polarimetric, 25-75 incidence angle)sample: snow

location: Mammoth Mountain, Sierra Nevada, Californiaground information:density, wetness, grain radius and surface roughness parametersinvestigations: comparison between measured and SAR-derived wetnessremarks: retrieval model for the volumetric liquid-water content in the top layer of

a wet snow pack

[44] Polarimetric Backscattering Measurements of Alpine Snowcover at 5.3 and 35 GHz

Strozzi T., C. MtzlerSubmitted to IEEE Trans. on Geosc. and Rem. Sens., 1996

instrument: NA-based scatterometers at 5.3 and 35 GHzincidence angle: 40polarization: HH, VV, HV, VHplatform height above ground: 4 m

sample: dry and wet snowcoverlocation: Weissfluhjoch, Davos, Switzerlandground information: temperature, depth, density, permittivity, grain shape and sizeinvestigations: seasonal variations of the backscattering coefficient

backscattering coefficient vs. snow depth, liquid-water content,thickness of a refrozen crust

remarks: -


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[45] Scatterometric Measurements of Snow Samples

Strozzi T., A. Wiesmann, C. Mtzler

in Ph. D. Thesis T. Strozzi, Institute of Applied Physics, University of Bern, 1996

instrument: scatterometer 35 GHzincidence angle: 50polarization: HH, VV, HV, VHtripod mounted

sample: homogeneous dry samples of snowcoverlocation: Weissfluhjoch, Davos, Switzerlandground information:snow temperature, sample thickness, density, permittivity, grain shape

and size, structural analysis with digitized snow sectionsinvestigations: backscattering coefficients vs. snow sample parameters

backscattering coefficients vs. snow sample thicknessremarks: disturbing effects of the edges of the snow samples

[46] Radar reflectivity of land at 94 GHz

Sume A.FOA Report C 30599-8.2,3.3, National Defense Research Establishment, Department ofInformation Technology, Linkping, Sweden, 1990.

instrument: incoherent radar at 94.5 GHz

depression angle: 4 - 54polarization: HH, VV, HV, VHtower-mountedheight above ground: 40 m

sample: terrain with trees and open groundsummer and winter conditions

location: near Mjlby, Swedenground information:-investigations: normalized radar cross section as a function of terrain type, depression

angle, polarization, and seasonimages of scene

remarks:

[47] The relation of millimeter-wavelength backscatter to surface snow properties

Williams L.D., J.G. GallagherIEEE Trans. Geosci. Remote Sensing, Vol. GE-25, No. 2, 1987.

instrument: pulsed radar at 94 GHzincidence angle: 2 - 72polarization: 6 combinations of linear and circular

RL, RR, VV, VH, 45/45, 45/-45helicopter-mounted


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sample: snowcover dry and wetlocation: flat, open soccer field

Bavaria, Germanyground information:snow surface roughness, liquid water content, grain size, porosity,

temperature profile

investigations: backscattering coefficient vs. incidence angleeffect of liquid water content, surface roughness, grain size

remarks: stepwise multiple regression

[48] Surface snow properties effects on millimeter-wave backscatter

Williams L.D., J.G. Gallagher, D.E. Sudgen, R.V. BirnieIEEE Trans. Geosci. Remote Sensing, Vol. 26, No.3, 1988.

instrument: radar at 94 GHz

depression angle: 15, 25, 35, 45, 55polarization: HH, VV, HV, VHheight above ground 25 m

sample: snowcover dry and wetlocation: flat, open soccer field in a mountain valley, Bavaria, Germanyground information: liquid water content, snow surface roughness, porosity, grain size and

shape, conductivity, pH, snow temperature profiles, densityinvestigations: backscattering coefficient vs. depression angle as function of snow

surface wetness and of wet snow surface roughnessremarks: for terrain covered by dry snow, the 94 GHz backscatter does not appear

to depend significantly on any of the measured snow properties

backscatter from wet snow is found to be sensitive to volumetric liquid-water content and surface roughness

[49] Millimetric radar backscatter from snowcover

Williams L.D., D.E. Sugden, R.V. BirnieFinal report to: Royal Signals and Radar Establishment, Malvern, United Kingdom on Ministryof Defense Agreement No. 2116/017.

instrument: pulsed radar at 94 GHz

incidence angle: 2 - 72polarization: 6 combinations of linear and circular

RL, RR, VV, VH, 45/45, 45/-45tower-mounted and helicopter-mountedheight above ground: 25 m [tower]

sample: snowcover under different conditions (melting, refreezing)location: flat, open soccer field in a mountain valley

Oberjettenberg, Bavaria, Germanyground information:snow surface liquid water content, snow temperature profile, density of

the upper centimeter of snow, snow surface roughness, for each layer:thickness, density, grain size and type, wetness, hardness, conductivity,

pHinvestigations: backscattering coefficient vs. incidence angle


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effect of liquid water content, surface roughness, grain size and surfacecrystal type

remarks: stepwise multiple regressionextensive description of ground information data

3.2 Passive microwave measurements

[50] Remote sensing of snowpack properties by microwave radiometry

Chang A.T.C.Hydrologic Applications of Space Technology (Proc. of the Cocoa Beach Workshop, Florida,August 1985). IAHS Publ., No. 160, 1986.

instrument: Nimbus-7 SMMR 37 GHz+ other radiometers

incidence angle: 50 [SMMR]polarization: H, V

sample: snowcoverlocation: Colorado Rockies, USA [other]

Central Russia, high plains of Canada [SMMR]ground information:-investigations: brightness temperature vs. snow depthremarks: -

[51] Snow property measurements correlative to microwave emission at 35 GHz

Davis R.E., J. Dozier, A.T.C. ChangIEEE Trans. Geosci. Remote Sensing, Vol. GE-25, No. 6, 1987.

instrument: radiometer 35 GHzpolarization: H, Vincidence angle: 10 - 70hand-held about 1 m above the snow

sample: from new snow, variable layered snow and melting snowlocation: Mammoth Mountain, Sierra Nevada, CA, USAground information:grain size, snow density, ice volume fraction, number and distances of

ice-pore and pore-ice transitions (by optical means), wetness, volumefraction of liquid water, temperature

investigations: brightness temperature vs. incidence angleeffect of liquid water content, different snow types

remarks: -

[52] Microwave radiometry of snow

Hallikainen M.COSPAR, Adv. Space Res., Vol. 9, No. 1, pp. 267-275, 1989.


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instrument: -sample: -location: -ground information:-

investigations: -remarks: review

[53] Results from ground-based radiometry of snow

Hallikainen M., V. Jskelainen, J. TalvelaIGARSS '89, Vol. 3, pp. 1231 - 1234, 1989.

instrument: radiometer 1, 16.5, 37 GHzincidence angle: 10 - 60

tower-mountedsample: snowcover various conditionslocation: Metshovi, Finlandground information:snow depth, snow water equivalent, density profile, snow temperature

profile, grain size, profile, snow layering information, transmission lossprofile of snow layer, ground temperature profile, weather data

investigations: brightness temperature as a function of time using vertical polarizationand 50 incidence anglebrightness temperature vs. crust deptheffect of snow water equivalent, structure and grain size;diurnal variations

remarks: semi-empirical brightness temperature model developed based onmeasured data

[54] Microwave Dielectric Properties of Surface Snow

Mtzler C., Aebischer H., Schanda E., 1984IEEE Journal of Oceanic Engineering, Vol. OE-9, No. 5., December 1984.

instrument: tower mounted radiometer (4.9, 10.4, 21, 35, 94 GHz ) (V and H po.)noise scaterrometer (10.4 GHz)

open-ended coaxial resonator (resonance frequency 1.4 GHz)sample: wet snowlocation: Alpine test site at Weissfluhjoch, Switzerlandground information:Operational ground data collected by SFISARinvestigations: the radiometer and dielectric data are used to derive spectra of complex

dielectric constants of wet snow between 1 and 100 GHzremarks: A way of resolving the contradicition between the resulting Deby

relaxation spectra (with a constant relaxation frequency of 9 GHz) and themixing formula of Polder and van Santen is presented

[55]


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Investigations on Snow Parameters by Radiometry in the 3- to 60-mm Wavelength Region

Hofer R. and C. MtzlerJournal of Geophysical Research, Vol. 85, No. C1., January 1980.

instrument: tower mounted radiometer (4.9, 10.4, 21, 35, 94 GHz)polarization: H, V

sample: snowcover (different types)location: Weissfluhjoch, Davos, Switzerlandground information:Operational ground data collected by SFISARinvestigations: brightness temperature vs. nadir angle

brightness temperature vs. frequencydiurnal variation of brightnesspenetration experiments

remarks: models for interpretation of penetration experiments ( absorption and

scattering coefficients)

[56] Analysis of brightness temperature of snow-covered terrain

Jskelinen V., M. HallikainenIGARSS '91

instrument: radiometer 1, 16.5, 37 GHz [tower]24, 34, 48 GHz [helicopter]

polarization: V, H

tower-mounted and helicopter-bornesample: snowcover

snow-covered terrain with different forest types dry and wetlocation: Metshovi, Finlandground information:investigations: effect of snow water equivalent, structure, grain size

effect of forest on brightness temperature of snow-covered terrain(see: FOREST AND TREES, "A multifrequency microwave radiometer",Panula-Ontto)

remarks: sensitivity analysishelicopter-borne experiment during SAAMEX-campaign, 1990

semi-empirical brightness temperature model developed based onmeasured data

[57] Terrain Radiation: Measurement Investigation at 94 GHz

N.V. Ruzhentsev, V.P. ChurilovInternational Journal of Infrared and Millimeter Waves, Vol. 17, No. 2, 1996

instrument: 94 GHz radiometeroperated on board of a helicopter and from towerhorizontal and vertical polarization

sample: various surface types including snowcover


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location:ground information:biogeophysical parameters of the surfacesinvestigations: brightness temperature vs. incidence angleremarks: snowcover measurements only discussed, no figures

[58] Passive Microwave Measurements of Tundra and Taiga Snow Covers in Alaska, U.S.A.

Sturm M., Grenfell T.C., D.K. PerovichAnnals of Glaciology 17, 1993

instrument: 18.7 and 37 GHzradiometers mounted on a 1.5 m tall bipodhorizontal and vertical polarization

sample: taiga and tundra snowlocation: Fairbanks and Imnaviat Creek (Alaska)

ground information:density, crystal structure and grain sizeinvestigations: effective emissivity vs. snow depthremarks: snow layers were removed

3.3 Combined active and passive microwave measurements

[59] Microwave Remote Sensing of Snowpack Properties: Potential and Limitations

Bernier P.Y.Nordic Hydrology, 18, 1987, 1-20

instrument: active and passive microwave systemssample: snowcover (overlying vegetation also discussed)location:ground information:investigations:remarks: review from a user's point of view of the possibilities and limitations of

microwave-based techniques for remote sensing of snowpack properties

[60] RASAM: A Radiometer-Scatterometer to Measure Microwave Signatures of Soil,

Vegetation and Snow

Hppi R.Ph.D.-Thesis, IAP University of Bern, 1987

instrument: active and passive microwave systems at 1.5, 2.5, 3.1, 4.6, 7.2, 10.2, 11GHz

polarization: H and V (radiometer)HH, VV, VH, HV (radar)

incidence angle: 0-80


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truck mountedsample: snowcover (soil and vegetation)location: Frutigen, Riffenmatt,ground information:snow depth, density, temperature, real part of the dielectric constantinvestigations: brightness temperature vs. frequency and incidence angle

backscattering coefficient vs. frequency and incidence angleremarks: Not all channels useful due to radio interference depending on location

[61] Towards the definition of Optimum Sensor Specifications for Microwave Remote

Sensing of Snow

Mtzler C., E. Schanda, W. GoodIEEE Trans. Geosc. Rem. Sens., Vol. 20, No. 1, January 1982

instrument: radiometer 1.8, 4.9, 10.4, 21, 36, 94 GHzpolarization: H, Vscatterometer 10.4 GHzpolarization: HH, VV, HV, VH

sample: snowcover different typeslocation: Weissfluhjoch, Davos, Switzerlandground information:Ground data collected by SFISARinvestigations: microwave response to the water equivalent of dry snow

microwave contrast between wet snow and snow-free landremarks: -

[62] Applications of the interaction of microwaves with the natural snow cover

Mtzler C.Remote Sensing Reviews, Vol. 2, pp. 259-387, 1987.

instrument: radiometer 4.9, 10.4, 21, 35, 94 GHzpolarization: H, Vscatterometer 10.4 GHzpolarization: HH, VV, HV, VHdielectric probes 0.3-1.4 GHz

sample: snowcover (different types)location: Weissfluhjoch, Davos, Switzerlandground information:Ground data collected by SFISAR; dielectric measurements of snow

(temperature, depth, water equivalent, density, snowtype)investigations: e.g. emissivities vs. frequency for different snow types

e.g. backscattering coefficient vs. incidence angle for different snow typesvariation of brightness temperatures during formation of a refrozen crustpenetration experiments

remarks: -

[63]


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Review of signature studies for microwave remote sensing of snowpacks

Mtzler C., R. HppiCOSPAR, Adv. Space Res., Vol. 9, No. 1, pp. 253-265, 1989.

instrument: radiometer 4.9, 10.4, 21, 35, 94 GHzpolarization: H, Vscatterometer 1.4 - 11 GHzpolarization: HH, VV, HV, VHdielectric probes 0.3-1.4 GHz

sample: snowcover (different types)location: Weissfluhjoch, Davos, Switzerlandground information:investigations: e.g.: emissivities vs. frequency for different (discriminated) snow types

e.g. backscattering coefficient vs. incidence angle for different

(discriminated) snow typesbrightness temperature vs. frequency, development during early stage ofsnow seasonvariation of brightness temperatures during formation of a refrozen crustbrightness temperature vs. crust thickness during formation

remarks: review of the following research topics:discrimination of different snow types, snow mapping, strongly layeredsnowpacks, inhomogeneous surface layers, determination of the liquidwater content, monitoring of melt-refreeze cycles, measurement of thecrust thickness, estimating the net energy loss, estimating the waterequivalent of a winter snowpack

[64] Microwave Snowpack Studies Made in the Austrian Alps During the SIR-C-X

Experiments in April 1994

Mtzler C., T. Weise, T. Strozzi, D. Floricioiu and H. RottResearch Report IAP No. 96-3, 1996

instrument: radiometer 21, 35 GHzpolarization: H, V

scatterometer 5.3, 35 GHz GHzpolarization: HH, VV, HV, VHdielectric sensors near 1 GHzSIR-C/X-SAR (1.25 and 5.3 GHz, polarimetric; 9.6 GHz VV-Pol.)

sample: snowcovers and glacierslocation: tztal, Austriaground information:extensiveinvestigations: profiles of the permittivity

emissivities vs. incidence anglediurnal variation of the brightness temperature of snow over a metal platebackscattering coefficient vs. incidence angle

scattering profilesSAR imagery and SAR derived backscattering coefficients vs. incidence


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angleremarks: -

[65] Microwave signatures of snow crusts: modeling and measurements

Reber B., C. Mtzler, E. SchandaInt. J. Remote Sensing, Vol. 8, No. 11, pp. 1649-1665, 1987

instrument: radiometer 4.9, 10.4, 21, 35, 94 GHzpolarization: H, V(scatterometer 1.4 - 11 GHzpolarization: HH, VV, HV, VH)

sample: snow sampleslocation: Weissfluhjoch, Davos, Switzerlandground information: thin sections, permittivity, snow and air temperature, density

investigations: effect of crustremarks: Modelling with Born Approximation

[66] Active and Passive Microwave Signatures of Antarctic Firn by Means of Field

Measurements and Satellite data

Rott H., K. Sturm, H. MillerAnnals of Glaciology, 17, 1993

instrument: radiometer 5.2 10.3 GHzpolarization: H, Vscatterometer 5.2, 10.3 GHzpolarization: HH, VV, HV, VHincidence angle 10-80ERS-1 (5.3 GHz, VV-Pol., 23 inc. angle)

sample: polar firnlocation: Dronning Maud Land, Antarcticaground information:accumulation rate, density, temperatureinvestigations: microwave penetration

backscattering coefficient and brightness temperature vs. inc. angle

remarks: -

[67] The Active and Passive Microwave Response to Snow Parameters. 1. Wetness

Stiles W.H., Ulaby F.T.Journal of Geophysical Research, Vol. 85, No. C2, pp. 1037-1044, February 20, 1980

instrument: radiometer 10.7, 37, 94 GHzpolarization H (V at 37 GHz)

scatterometer 1-18 GHz and 35.6 GHzpolarization HH, HV, VV (RR,RL,LL at 35 GHz)


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incidence angle 0-80truck-mounted

sample: snowlocation: near Steamboat Springs, Coloradoground information:snow parameters at approximately 2 hours intervals

investigations: diurnal observations of the variation of the backscattering coefficient andof the apparent radiometric temperature with snow wetness

remarks: -

[68] Ground-based experiments of snow for validation of ERS-1 SAR data

Strozzi T., T. Weise, C. MtzlerReport, Institute of Applied Physics, University of Bern, 1992

instrument: scatterometers at 2.6 and 35 GHz

polarization: HH, VV, HV, VHradiometers at 21 and 35 GHzpolarization: H, Vincidence angle: 30-70truck mounted platform

sample: snowcover melting-refreezinglocation: Stilli, Davos, Switzerland

Amherst, MA, USAground information:snow temperature, depth, density, structure, grain sizeinvestigations: backscattering coefficients vs. incidence angle

diurnal variations of backscattering coefficientdiurnal variation of reflectivity

remarks: -

[69] The Active and Passive Microwave Response to Snow Parameters. 2.. Water Equivalent

of Dry Snow

Ulaby F.T., Stiles W.H.Journal of Geophysical Research, Vol. 85, No. C2, pp. 1045-1049, February 20, 1980

instrument: radiometer 10.7, 37, 94 GHzpolarization H (V at 37 GHz)scatterometer 1-18 GHz and 35.6 GHzpolarization HH, HV, VV (RR,RL,LL at 35 GHz)incidence angle 0-80truck-mounted

sample: snowlocation: near Steamboat Springs, Coloradoground information:density, temperature, snow depthinvestigations: measurements of the variation of the backscattering coefficient and of the

emissivity with water equivalent of dry snowremarks: snow pile experiments

comparison with a simple semi-empirical scattering and emission model


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[70] Microwave Remote Sensing: Active and Passive

Ulaby F.T., R.K. Moore, A.K. Fung

Reading, MA: Addison-Wesley, Vol. I (1981) + II (1982) + III (1986)

3.4 Dielectric measurements and models

[71] Microwave Effective Permittivity Model of Media of Dielectric Particles and

Applications to Dry and Wet Snow

Boyarskii D.A., Tikhonov V.V.Proceedings IGARSS'94, pp. 2065-2067

frequency: 1-37 GHz rangesample: dry snow, wet snowlocation: -independent data: -investigations: dielectric modelremarks: comparison of model results with the experimental data of 0]

[72] Snow Dielectric Measurements

Denoth A.Adv. Space Res., Vol. 9, No.1, pp. (1)233-(1)243,1989

frequency: different measurements techniques from 100 Hz to 10 GHzsample: Alpine snowlocation: Stubai Alps (?)independent data: snow porosity, grain size and shape, snow wetness, densityinvestigations: dielectric constant and dielectric lossremarks: -

[73] Review of the microwave dielectric and extinction properties of sea ice and snow

Hallikainen M.Proceedings IGARSS '92, pp.961-965.

frequency: 0.5 to 40 GHz rangesample: sea ice and snowlocation: -independent data: -investigations: experimental dielectric and extinction/absorption properties

remarks: review with main references


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[74] Dielectric Properties of Snow in the 3 to 37 GHz Range

Hallikainen M., Ulaby F.T., Abdelrazik M.IEEE Transactions on Antennas and Propagation, Vol. AP-34, No. 11, November 1986,pp.1329-1339.

frequency: 3 to 18 GHz range, 37 GHzsample: dry and wet snowlocation: open areaindependent data: -investigations: dielectric measurements for the following parametric ranges: liquid water

content 0 to 12.3 percent by volume, snow density 0.09 to 0.42 g cm-3 ,temperature 0 to -15C, crystal size 0.5 to 1.5 mm. Comparison with anempirical (Deby-like) and a theoretical (Polder-Van Santen) model.

remarks: -

[75] Extinction Behavior of Dry Snow in the 18- to 90-GHz Range

Hallikainen M., Ulaby F.T., van Deventer T.E.IEEE Transactions on Geoscience and Remote Sensing, Vol. GE-25, No. 6, November 1987,pp. 737-745

frequency: 18-90 GHz rangesample: several natural dry snow types (fresh, refrozen)location: laboratory conditions (HUT)independent data: snow density, average grain size, surface roughnessinvestigations: measurements of transmission loss as a function of sample thickness;

extinction coefficient; surface scattering loss. Comparison of experimentaldata with model according to the strong fluctuation theory.

remarks: -

[76] Snow Probe for In Situ Determination of Wetness and Density

Kendra J.R., F.T. Ulaby, K. SarabandiIEEE Trans. Geosc. Rem. Sens., Vol. 32, No. 6, November 1994

instrument: hand held electromagnetic sensor near 1 GHz (resonance frequency)sample: snow probeslocation: laboratory conditionsindependent data: liquid water content, snow densityinvestigations: complex dielectric constant of the snow medium

retrieval of snow density and of liquid-water content by means of empiricaland semi-empirical models

remarks: -


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[77] Dielectric properties of ice and snow at 26.5 to 40 GHz

Koh G.

Proc. IGARSS '92, pp. 820-822.

instrument: step frequency radar at 26.5 to 40 GHzsample: snow sampleslocation: Greenland ice sheetindependent data: snow volume fractioninvestigations: wave velocity and attenuation

relative permittivity and extinction lossremarks: minimum penetration depth of 87 cm into the firn

[78] Dielectric Permittivity and Scattering Measurements of Greenland Firn at 26.5-40 GHz

Lytle V.I., K.C. JezekIEEE Trans. Geosc. Rem. Sens., Vol. 32, No. 2, March 1994

instrument: reflectometer arrangement (step frequency radar) 26.5 - 40 GHzsample: snow and ice sampleslocation: Firn samples from the north central Greenlend ice sheet, snow samplesfrom around Hanover, NH, USA.independent data: Number of snow layers, snow grain size, density, depth.investigations: propagation velocity and attenuation of generated pulses

effect of ice volume fraction. Estimation of scattering loss through snowsample.remarks -

[79] Microwave Properties of Ice and Snow

Mtzler C.International Symposium on Solar System Ices, Toulouse, France, 27-30 March 1995

instrument: -sample: ice and dry snowlocation: -independent data: -investigations: -remarks: review paper

[80] Microwave Permittivity of Dry Snow

Mtzler C.IEEE Trans. Geosc. Rem. Sens., Vol. 34, No. 2, March 1996

instrument: resonator near 1 GHz


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sample: 90 different types of dry snow (fresh, old, wind-pressed , depth hoar,refrozen crust), temperature -8 to -1C

location: Weissfluhjoch-Davos (Swiss Alps) and Getschalp-Kaunertal (AustrianAlps)

independent data: density

investigations: Measurements of the permittivity of dry snow with a specially designedresonator, interpretation of data in terms of physical mixing theory (Polder-van Santen model)

remarks: Derivations of axial radius of grains as a function of snow density

[81] Dielectric properites of fresh-water ice at microwave frequencies

Mtzler C., Wegmller U.J. Phys. D: Appl. Phys 20 (1987) pp. 1623-1630, Erratum Vol. 21 (1988) p.1660.

instrument: resonator (2-10 GHz), radiometer (10-100 GHz)

sample: pure and slightly saline ice at different temperatureslocation: -independent data: -investigations: Measurements for the 2-100 GHz range. Influence of small impurities isdiscussed.remarks: Comparison with review of Warren (1984).

[82] Snow Fork for Field Determination of the Density and Wetness Profiles of a Snow Pack

Shivola A., Tiuri M.IEEE Trans. on Geoscience and Remote Sensing, Vol. GE-24, No. 5, September 1986. pp. 717-721.

instrument: Snow fork at 1 GHz resonance frequencysample: natural dry and wet snowlocation: -independent data: -investigations: technical paper presenting the design and use of a snow fork for measuring

density and wetness profiles of s snow packremarks: -

[83] The Complex Dielectric Constant of Snow at Microwave Frequencies

Tiuri M., Shivola A., Nyfors E.G., Hallikainen M.T.IEEE Journal of Oceanic Engineering, Vol. OE-9, No. 5, December 1984, pp. 377-382

instrument: cylindrical cavity sensors. f = 850 MHz, 1.9 GHz, 5.6 GHz and 12.6 GHz.sample: coarse old, aged, new fine-grained, undisturbed and prepared snowlocation: laboratory conditions (HUT)independent data: -investigations: Measurements of the complex dielectric constant of snow at microwave

frequencies. Nomograph for determining the density and wetness of wetsnow from its dielectric constant.

remarks: Extension of density range by compressing snow samples.


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3.5 Visible and infrared measurements and models1

Snow and Ice Spectra

Until recently, frost and snow spectra were calculated using the optical constants of ice in a Mietheory and radiative transfer model (Dozier and Warren, 1982). Field measurements (Warren et al.,

1986) show that the Dozier and Warren Model is accurate in the visible near infrared (VNIR2) and the

short wave infrared (SWIR) regions. The directional hemispherical reflectance spectra were recently

measured for the first time in the medium wavelength infrared (MWIR) and thermal infared (TIR)

bands (Salisbury et al., 1994), and they find that the calculated spectra for frost are correct, but

calculated snow spectra are in error by up to 6%, depending on grain size and degree of cementation

(sintering). They also developed an improved scattering model to explain the differences (Wald,

1994). As might be expected, the measurement of the spectrum of smooth ice agrees with that

calculated from the Fresnel equations.

Directional reflectance and emittance (bidirectional reflectance distribution function, BRDF) for frost

and snow have been calculated with the same models used to calculate spectra. Again, the Dozier and

Warren (1982) model appears accurate in the VNIR and SWIR range, and we find little difference

between the results of their model and that of Wald (1994) for loose snow grains, which have

Lambertian-type behavior at all wavelengths. Crusted snow, however, has a very strong specular

component in the thermal infrared, as discussed more fully below. Smooth, clear ice, of course, isspecular at all wavelengths.

Vegetation Spectra

Vegetation is quoted here as the main object class to which snow has to be discriminated. The spectral

properties of individual leaves have been well understood for quite a long time (e.g., Gates et al.,

1965), especially in the VNIR and SWIR. Until recently, laboratory instrumentation was not available

to make equivalent measurements in the thermal infrared, but recent spectroscopic studies have

provided confirmation of general information derived from earlier broad-band measurements

(Salisbury and Milton, 1988). Although leaf spectra are readily available, good canopy spectra are

not, because of the technical difficulty of making such measurements. In the reflective part of the

1Input to Chapter 3.5 was taken from ftp://rocky.eps.jhu.edu/pub/veg%26snow/VEG%26SNOW.TXT,

which was written and last updated by J.W. Salisbury (February 28, 1996), Department of Earth and Planetary

Science, John Hopkins University, Laurel, MD 20723.


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spectrum, difficulties arise - particularly in the SWIR - from the strong water vapor absorption bands

in solar radiation illuminating the canopy, which leave large gaps in the spectrum where they absorb

completely, and introduce observational difficulties even where they do not (Biehl et al., 1984).

Atmospheric absorption of emitted radiation is a problem in the thermal infrared region, along with anhistorical limitation on the availability of portable field spectrometers. These difficulties have been at

least partially remedied by spectral measurements of leaf piles and canopy parts in the laboratory to

provide simulated canopy spectra relatively untroubled by water vapor absorption (e.g., Salisbury and

Milton, 1988).

Ever since the pioneering measurements of directional scattering properties of individual leaves by

Breece and Holmes (1971), gradually more sophisticated models of canopy scattering have been

developed, as best summarized by Kimes (1984). Such models are not simple, because canopy

scattering is complicated by the fact that individual leaf reflectances vary with wavelength, from

predominantly surface scattering in the visible and TIR regions, to predominantly volume scattering in

the near infrared (NIR) and SWIR regions; and typical leaf orientation varies during different growth

stages for a given species, and from one species to another.

To provide real data input to such models, Goddard Space Flight Center developed a sphere-scanning

radiometer, called the PARABOLA, for field measurements of the BRDF of natural surfaces (Deering

and Leone,1984). This field instrument typically measures BRDF in three narrow bandpasses in thevisible, near-infrared, and short-wave infrared. Typical scattering data for soils and vegetation have

been summarized by Deering (1989), and have been made available by Don Deering in digital form.

Other field measurements have been made in the VNIR and SWIR regions of the spectrum by Ranson

et al. (1985). Few measurements of directional emittance have been made because of the

unavailability, until recently, of appropriate field instruments. We have made field measurements that

show that conifers are Lambertian emitters because of the strong canopy scattering produced by

randomly-oriented needles. However, some preliminary measurements by others appear to show

small, but inconsistent, directional effects on grass canopy emissivities (Norman and Balick, 1992),

which may be due to the quasi-parallel surfaces produced by the bent tips of long grass. Such

directional effects could be even greater for deciduous leaf canopies, where leaf orientations tend to

be more horizontal (depending on species).

Spectral behavior of frost, snow and ice

Our thermal infrared directional hemispherical reflectance measurements of frost and snow (Salisbury

et al., 1994) were matched at 2.0 m with VNIR/SWIR spectra calculated using the Dozier and


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Warren (1982) delta Eddington model. The grain size of our frost is not given, and our snow spectra

are labeled simply "fine", "medium granular", and "coarse granular". Precise grain sizes are not given

because, as explained more fully in Salisbury et al. (1994), and Wald (1994), grain shape, size range,

and cementation effects make a single grain size description misleading. However, the VNIR/SWIRdelta Eddington calculation uses a single grain size. The single "effective" grain size that matched the

reflectance of our measured samples at 2 m wavelength was 10 m for the frost, 24 m for the fine

snow, 82 m for the medium granular snow, and 178 m for the coarse granular snow. The physical

grain size of the granular snow was much larger under the microscope, averaging about 400 m and

1500 m for the medium and coarse granules, respectively. The optical grain size was much smaller,

because it is the size of spheres with the same surface to volume ratio as is present in the real snow.

Recently, Mtzler (1997) showed that the grain size defined in this way corresponds to the correlation

length, and this quantity is close to the minimum extent of the typical snow grains.

Caveat: As is typical for aged snow, medium and coarse granular snow grains are cemented into a

crust, which introduces a strong specular reflectance component in the thermal infrared, as discussed

briefly above. In fact, we find that as snow ages and grains become larger and more completely

cemented together into a continuous crust, snow approaches the spectral and directional behavior of

ice in the thermal infrared. It should be noted here that, just as crusted snow resembles ice in its

spectral and BRDF behavior in the thermal infrared, ice tends to resemble coarse, crusted snow in the

VNIR/SWIR. That is, smooth, clear ice has an extremely low reflectance in the VNIR/SWIR, formingwhat is called "black ice", which is rare. Natural ice typically has some snow on its surface, and/or the

surface is rough, and its interior contains grain boundaries and air bubbles. The presence of these

scattering centers results in strong diffuse scattering, especially in the VNIR. As the wavelength

increases beyond the scale of these scattering centers, and predominantly volume scattering is

replaced by surface scattering, the BRDF changes from largely diffuse in the VNIR/SWIR to largely

specular in the thermal infrared. Thus, an analyst should not use the spectral and scattering

characteristics of smooth ice for an ice-covered surface in the VNIR/SWIR, except under unusual

(black-ice) circumstances. Most ice has the spectral and BRDF properties of our coarse, granular,

crusted snow in both reflectance and emittance. Both frost and fresh, fine snow should be Lambertian

at all wavelengths, just as smooth, clear ice should be specular at all wavelengths. Aged, crusted snow

should be predominantly Lambertian in the VNIR/SWIR and predominantly specular in the thermal

infrared.


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Spectral behaviour of vegetation

Spectra were assembled from two segments; the VNIR and SWIR comprising segment one, and the

MWIR and TIR comprising segment two. The first segment for trees used simulated canopy spectrameasured by Barry Rock of the University of New Hampshire on leaf piles and canopy parts using a

GER IRIS Mark IV field spectrometer in the laboratory. The tree leaves or branches were illuminated

from directly above and measured at a reflectance angle of about 30. The grass VNIR/SWIR

spectrum was measured in the laboratory at JHU, also with a GER IRIS Mark IV, using a large piece

of fresh sod. The grass was illuminated from directly above and measured at a reflectance angle of

60 to avoid viewing the thatch. The artificial illumination sources used by emit much less radiation

in the blue region of the spectrum than does the sun. This results in an instrumental artifact in GER

IRIS Mark IV spectra, characterized by an apparent increase in reflectance of the sample from the

blue into the UV (the so-called "blue tail"). Spectra of vegetation measured outdoors are not affected

in the blue region by atmospheric water vapor absorption, and so have been used to check the true

reflectance spectra of vegetation, which actually decline through the blue and into the UV. Thus, the

VNIR/SWIR segments of the vegetation spectra were corrected by hand to remove the blue tail before

being joined with the thermal infrared segments. The thermal infrared segments were generated from

directional hemispherical reflectance spectra of needle and leaf piles. Conifer needles, deciduous tree

leaves and grass blades all have a very low reflectance (high emissivity) throughout the thermalinfrared range, although the conifer needles are consistently lower in reflectance than the other two.

There are subtle spectral features associated with differences in cuticular waxes that could be

diagnostic of deciduous species in the laboratory (Salisbury and Milton, 1988). The diagnostic

differences in these features vary, however, typically only about 2%, and canopy scattering will

further reduce this spectral contrast by a factor of at least two. Thus, spectral features are of interest

for laboratory applications, but not usually for remote sensing. Because spectral features are so

subdued, we selected one typical deciduous leaf spectrum to represent all deciduous species, one

conifer to represent all conifers, and one grass species to represent all grasses. Each thermal infrared

spectrum was reduced in reflectance by a factor of two to conservatively account for canopy

scattering (conifers, in particular, should undergo more intense scattering, and field measurements

show conifers to exhibit black body behavior within measurement error of about 1%). The thermal

infrared segments were then joined with the VNIR/SWIR segment of the appropriate species by

making a straight-line interpolation between 2.5 and 3.0 m.

4.


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Identified problem areas and data gaps

There appear to be few parts of the visible and infrared spectrum that have insufficient knowledge to

complete a database. The question is whether the database contains sufficient knowledge for differentsnow types, snow depths, illumination types, vegetation cover etc. The main problem with insufficient

knowledge is the lack of data for an accurate characterization of the snowpack, especially grain size,

but also impurities.

In the case of microwave measurements of snow, there is a lack of generally available instruments to

measure the key parameters relevant for microwave remote sensing, specifically:

Snow structure (grain size, and shape, correlation length) Liquid water content and its profilesSome investigations were performed with artificially treated snow (e.g. [32]). It is unclear, how well

these experiments are useful for assessing signatures of the natural snow. On the other hand, naturally

disturbed snow surfaces are rarely investigated topics. Since snow measurements in the microwave

portion of the electromagnetic spectrum exhibit a highly sensitive response to local density variations,

disturbed snow (e.g. by hail, from avalanches, by snow falling from trees, by creeping on slopes) mayproduce special microwave signatures. Such studies of special features have not yet been reported.

Generally, the distinct microwave signatures of snow is the outcome of the influence of the following

parameters:

Instrumental parameters (frequency range, incidence angle range, polarization) Geographical area (terrain, underlying snow, vegetation) Climatic area Temporal scale: season, short and longtime variations Typical/special snow conditionsA large number of snow types can develop in areas where snow is persistent for months. Effects by

wind, solar radiation, vapor transfer, melting and refreezing and different kinds of precipitation can

interact in many ways. Apart from selected special cases the observation so far have been

concentrated on typical snowcover conditions as they appear in the investigated areas.


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The usefulness of a snow signatures for a specific remote sensing application depends on The quality of the microwave measurements The existence, quality and completeness of ground information. Following the definition of the

term "microwave signature" in Chapter 1, a simple brightness temperature as measured at a certainfrequency and incidence angle without proper knowledge of the state of the object (i.e. ground

information) cannot be regarded as a useful signature. Nevertheless, such data should not be

discarded. Additional ground information for certain measurements may exist (e.g. from

operational weather stations), but one may be unaware of them or they may be not readily

accessible.

The application and the parameters to be retrieved. For example, certain applications may requiresignatures at less frequencies, while other require full information at all frequencies and

polarizations.

Several problems arise when addressing the uniqueness and representativity of a specific snow

signature:

At which level can we call a signature complete? For a certain case, measurements at one

frequency may not have been performed (e.g. due to an instrumental failure). Should we discard

such a signature, interpolate the missing value or extract it from a model? Different research groups may use different instrumental parameters (e.g. frequency). For certain

measurement objects, this may pose a serious problem when comparing measurements from

different sources, while for other objects the differences may be neglected.

Having these considerations in mind, an exhaustive and detailed overview of data gaps covering the

whole range of observational parameters and natural objects can not be presented in this review. This

is the scope of a following report. However, a few examples of data gaps are identified already now:

Active microwave measurements of snow-free and snow-covered, frozen, rocky or grassy ground

at 5.3 and 35 GHz are missing in the catalogue [3]. Passive microwave measurements of these

objects were performed with radiometers by Christian Mtzler [11], [12], [13].

The influence of the vegetation (grass, shrubs, short vegetation and trees) on the radarmeasurements of snowcover was never studied in detail. Similar passive microwave measurements

were instead probably performed at HUT.

The influence of the underlying ground is very important. The experiments tend to concentrate


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only on the snow, and little attention is given to the ground. Therefore, contrasting results

regarding the effect of the depth of dry snow were for instance observed by Strozzi [1], [2] and

Kendra [28] at C-Band.

There are virtually no signatures of naturally disturbed snow, e.g. by wind drift, precipitation (rain,hail) and avalanches.

5.


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References

5.1 Microwave and dielectric measurements

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[2] Ulaby F.T. and Dobson M.C., 1989: Handbook of Radar Scattering Statistics for Terrain.Artech House Inc., Norwood, MA.

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[8] Nashashibi A., F.T. Ulaby and K. Sarabandi, 1996: Measurement and Modeling of theMillimeter-Wave Backscatter Response of Soil Surfaces. IEEE Trans. on Geosc. and Rem.

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[10] Hallikainen M., F.T. Ulaby, and T.E. van Deventer, 1987: Extinction behavior of dry snow inthe 18- to 90-GHz range. IEEE Trans. Geosci. Remote Sensing, Vol. GE-25, No. 6, pp. 737-


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745.

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[18] Baars E.P., H. Essen, 1988: Millimeter-wave backscatter measurements on snow-coveredterrain. IEEE Trans. Geosci. Remote Sensing, Vol. 26, No. 3, pp. 282-299.

[19] Berger R., Layman R., Van Zandt T., Walsh J., Knox J., 1985: Observations of the backscatterfrom snow at millimeter wavelengths. In: Snow Symposium V, Hanover, New Hampshire,

August 1985. Vol. 1., U.S. Army Cold Regions Research and Engineering Laboratory,

Hanover, NH, CRREL Special Report 86-15, pp. 311-316.[20] Bernier M. and J.-P. Fortin, 1995: The Potential of Time Series of C-Band SAR Data to

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Pasadena, CA, pp. 1932-1934.

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[26] Haddock T.F., and F.T. Ulaby, 1990: 140-GHz scatterometer system and measurements ofterrain. IEEE Trans. Geosci. Remote Sensing, Vol. 28, No. 4, pp. 492-499.

[27] Hallikainen, M., Pulliainen, J., Radar polarimeter measurements of snow. Digest 1989 IEEEInernational Geoscience and Remote Sensing Symposium (IGARSS'89), pp. 1829-1831,

Vancouver, Canada, 10-14 July 1989.

[28] Hayes D.T., U.H.W. Lammers, and R.A. Marr, 1984: Scattering from snow backgrounds at 35,98, and 140 GHz. Report RADC-TR-84-69, Rome Air Development Center, Air Force Systems

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[32] Linlor W. I., 1980: Permittivity and attenuation of wet snow between 4 and 12 GHz. J. Appl.Phys. 51(5), May 1980, pp. 2811-2816.

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[hydrology] research and development of remote sensing methods

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